Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations27,348
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 MiB
Average record size in memory304.0 B

Variable types

Numeric27
Categorical10

Alerts

AP3_2_Settings_LeverArm_Machine_M355_Z_mm has constant value "141.55" Constant
AP3_2_Settings_M358_Montage_Position_mm has constant value "1.9" Constant
AP3_2_Settings_M359_Montage_Position_mm has constant value "-17.05" Constant
AP3_2_Actual_Part_to_Service is highly overall correlated with PARAM_Inclination90ToBeltDirectionOffset__deg_ and 1 other fieldsHigh correlation
AP3_2_Settings_LeverArm_Machine_M353_Y_mm is highly overall correlated with AP3_2_Settings_LeverArm_Machine_M354_X_mmHigh correlation
AP3_2_Settings_LeverArm_Machine_M354_X_mm is highly overall correlated with AP3_2_Settings_LeverArm_Machine_M353_Y_mmHigh correlation
PARAM_Inclination90ToBeltDirectionOffset__deg_ is highly overall correlated with AP3_2_Actual_Part_to_Service and 14 other fieldsHigh correlation
PARAM_InclinationBeltDirectionOffset__deg_ is highly overall correlated with AP3_2_Actual_Part_to_Service and 14 other fieldsHigh correlation
PARAM_M826FirstPosition__mm_ is highly overall correlated with PARAM_Inclination90ToBeltDirectionOffset__deg_ and 19 other fieldsHigh correlation
PARAM_M826InsertLockPinPosition__mm_ is highly overall correlated with PARAM_Inclination90ToBeltDirectionOffset__deg_ and 19 other fieldsHigh correlation
PARAM_M826SecondPosition__mm_ is highly overall correlated with PARAM_Inclination90ToBeltDirectionOffset__deg_ and 19 other fieldsHigh correlation
RESULT_Inclination90ToBeltDirection__deg_ is highly overall correlated with RESULT_MaximalForceAngle__deg_ and 9 other fieldsHigh correlation
RESULT_InclinationBeltDirection__deg_ is highly overall correlated with RESULT_InclinationInMaximalForceDirection__deg_ and 7 other fieldsHigh correlation
RESULT_InclinationInMaximalForceDirection__deg_ is highly overall correlated with RESULT_InclinationBeltDirection__deg_ and 7 other fieldsHigh correlation
RESULT_MaximalForceAngle__deg_ is highly overall correlated with RESULT_Inclination90ToBeltDirection__deg_ and 9 other fieldsHigh correlation
RESULT_Perpendicularity__mm_ is highly overall correlated with RESULT_Inclination90ToBeltDirection__deg_ and 9 other fieldsHigh correlation
RESULT_XAngleMax__deg_ is highly overall correlated with PARAM_Inclination90ToBeltDirectionOffset__deg_ and 22 other fieldsHigh correlation
RESULT_XAngle__deg_ is highly overall correlated with PARAM_Inclination90ToBeltDirectionOffset__deg_ and 22 other fieldsHigh correlation
RESULT_XWheelDownAngle__deg_ is highly overall correlated with PARAM_Inclination90ToBeltDirectionOffset__deg_ and 22 other fieldsHigh correlation
RESULT_XWheelDownAverageDiff__mm_ is highly overall correlated with PARAM_Inclination90ToBeltDirectionOffset__deg_ and 22 other fieldsHigh correlation
RESULT_XWheelDownAverageSensor1__mm_ is highly overall correlated with RESULT_Inclination90ToBeltDirection__deg_ and 11 other fieldsHigh correlation
RESULT_XWheelDownAverageSensor2__mm_ is highly overall correlated with RESULT_XWheelDownAverageSensor1__mm_ and 2 other fieldsHigh correlation
RESULT_XWheelUpAngle__deg_ is highly overall correlated with PARAM_Inclination90ToBeltDirectionOffset__deg_ and 22 other fieldsHigh correlation
RESULT_XWheelUpAverageDiff__mm_ is highly overall correlated with PARAM_Inclination90ToBeltDirectionOffset__deg_ and 22 other fieldsHigh correlation
RESULT_XWheelUpAverageSensor1__mm_ is highly overall correlated with RESULT_Inclination90ToBeltDirection__deg_ and 11 other fieldsHigh correlation
RESULT_XWheelUpAverageSensor2__mm_ is highly overall correlated with RESULT_XWheelDownAverageSensor1__mm_ and 2 other fieldsHigh correlation
RESULT_ZAngleMax__deg_ is highly overall correlated with PARAM_M826FirstPosition__mm_ and 19 other fieldsHigh correlation
RESULT_ZAngle__deg_ is highly overall correlated with PARAM_M826FirstPosition__mm_ and 19 other fieldsHigh correlation
RESULT_ZWheelDownAngle__deg_ is highly overall correlated with PARAM_M826FirstPosition__mm_ and 11 other fieldsHigh correlation
RESULT_ZWheelDownAverageDiff__mm_ is highly overall correlated with PARAM_M826FirstPosition__mm_ and 11 other fieldsHigh correlation
RESULT_ZWheelDownAverageSensor3__mm_ is highly overall correlated with PARAM_Inclination90ToBeltDirectionOffset__deg_ and 17 other fieldsHigh correlation
RESULT_ZWheelDownAverageSensor4__mm_ is highly overall correlated with PARAM_Inclination90ToBeltDirectionOffset__deg_ and 19 other fieldsHigh correlation
RESULT_ZWheelUpAngle__deg_ is highly overall correlated with PARAM_M826FirstPosition__mm_ and 19 other fieldsHigh correlation
RESULT_ZWheelUpAverageDiff__mm_ is highly overall correlated with PARAM_M826FirstPosition__mm_ and 19 other fieldsHigh correlation
RESULT_ZWheelUpAverageSensor3__mm_ is highly overall correlated with PARAM_Inclination90ToBeltDirectionOffset__deg_ and 17 other fieldsHigh correlation
RESULT_ZWheelUpAverageSensor4__mm_ is highly overall correlated with PARAM_Inclination90ToBeltDirectionOffset__deg_ and 21 other fieldsHigh correlation
AP3_2_Settings_LeverArm_Machine_M353_Y_mm is highly imbalanced (62.5%) Imbalance
AP3_2_Settings_LeverArm_Machine_M354_X_mm is highly imbalanced (94.6%) Imbalance
RESULT_XWheelUpAverageSensor1__mm_ has 306 (1.1%) zeros Zeros
RESULT_XWheelDownAverageSensor1__mm_ has 640 (2.3%) zeros Zeros
RESULT_ZWheelDownAverageDiff__mm_ has 587 (2.1%) zeros Zeros
RESULT_ZWheelDownAngle__deg_ has 297 (1.1%) zeros Zeros
RESULT_ZAngle__deg_ has 827 (3.0%) zeros Zeros
RESULT_InclinationBeltDirection__deg_ has 739 (2.7%) zeros Zeros
RESULT_InclinationInMaximalForceDirection__deg_ has 1664 (6.1%) zeros Zeros

Reproduction

Analysis started2025-03-07 15:23:10.187437
Analysis finished2025-03-07 15:24:42.956831
Duration1 minute and 32.77 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Palette
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4987202
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size427.3 KiB
2025-03-07T16:24:43.189734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2916485
Coefficient of variation (CV)0.5094001
Kurtosis-1.2374548
Mean4.4987202
Median Absolute Deviation (MAD)2
Skewness0.00050032119
Sum123031
Variance5.251653
MonotonicityNot monotonic
2025-03-07T16:24:43.315839image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
6 3442
12.6%
4 3429
12.5%
1 3429
12.5%
8 3424
12.5%
3 3418
12.5%
2 3410
12.5%
5 3402
12.4%
7 3394
12.4%
ValueCountFrequency (%)
1 3429
12.5%
2 3410
12.5%
3 3418
12.5%
4 3429
12.5%
5 3402
12.4%
6 3442
12.6%
7 3394
12.4%
8 3424
12.5%
ValueCountFrequency (%)
8 3424
12.5%
7 3394
12.4%
6 3442
12.6%
5 3402
12.4%
4 3429
12.5%
3 3418
12.5%
2 3410
12.5%
1 3429
12.5%

AP3_2_Settings_LeverArm_Machine_M353_Y_mm
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size427.3 KiB
87.3
22236 
87.0
3886 
87.1
 
1058
87.05
 
167
87.2
 
1

Length

Max length5
Median length4
Mean length4.0061065
Min length4

Characters and Unicode

Total characters109,559
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row87.3
2nd row87.3
3rd row87.3
4th row87.3
5th row87.3

Common Values

ValueCountFrequency (%)
87.3 22236
81.3%
87.0 3886
 
14.2%
87.1 1058
 
3.9%
87.05 167
 
0.6%
87.2 1
 
< 0.1%

Length

2025-03-07T16:24:43.488164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-07T16:24:43.599782image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
87.3 22236
81.3%
87.0 3886
 
14.2%
87.1 1058
 
3.9%
87.05 167
 
0.6%
87.2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
8 27348
25.0%
7 27348
25.0%
. 27348
25.0%
3 22236
20.3%
0 4053
 
3.7%
1 1058
 
1.0%
5 167
 
0.2%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82211
75.0%
Other Punctuation 27348
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 27348
33.3%
7 27348
33.3%
3 22236
27.0%
0 4053
 
4.9%
1 1058
 
1.3%
5 167
 
0.2%
2 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 27348
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 109559
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 27348
25.0%
7 27348
25.0%
. 27348
25.0%
3 22236
20.3%
0 4053
 
3.7%
1 1058
 
1.0%
5 167
 
0.2%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 109559
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 27348
25.0%
7 27348
25.0%
. 27348
25.0%
3 22236
20.3%
0 4053
 
3.7%
1 1058
 
1.0%
5 167
 
0.2%
2 1
 
< 0.1%

AP3_2_Settings_LeverArm_Machine_M354_X_mm
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size427.3 KiB
159.8
27181 
160.2
 
167

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters136,740
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row159.8
2nd row159.8
3rd row159.8
4th row159.8
5th row159.8

Common Values

ValueCountFrequency (%)
159.8 27181
99.4%
160.2 167
 
0.6%

Length

2025-03-07T16:24:43.707403image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-07T16:24:43.791328image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
159.8 27181
99.4%
160.2 167
 
0.6%

Most occurring characters

ValueCountFrequency (%)
1 27348
20.0%
. 27348
20.0%
5 27181
19.9%
9 27181
19.9%
8 27181
19.9%
6 167
 
0.1%
0 167
 
0.1%
2 167
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 109392
80.0%
Other Punctuation 27348
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 27348
25.0%
5 27181
24.8%
9 27181
24.8%
8 27181
24.8%
6 167
 
0.2%
0 167
 
0.2%
2 167
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 27348
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 136740
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 27348
20.0%
. 27348
20.0%
5 27181
19.9%
9 27181
19.9%
8 27181
19.9%
6 167
 
0.1%
0 167
 
0.1%
2 167
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136740
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 27348
20.0%
. 27348
20.0%
5 27181
19.9%
9 27181
19.9%
8 27181
19.9%
6 167
 
0.1%
0 167
 
0.1%
2 167
 
0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size427.3 KiB
141.55
27348 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters164,088
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row141.55
2nd row141.55
3rd row141.55
4th row141.55
5th row141.55

Common Values

ValueCountFrequency (%)
141.55 27348
100.0%

Length

2025-03-07T16:24:43.882333image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-07T16:24:43.986474image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
141.55 27348
100.0%

Most occurring characters

ValueCountFrequency (%)
1 54696
33.3%
5 54696
33.3%
4 27348
16.7%
. 27348
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 136740
83.3%
Other Punctuation 27348
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 54696
40.0%
5 54696
40.0%
4 27348
20.0%
Other Punctuation
ValueCountFrequency (%)
. 27348
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 164088
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 54696
33.3%
5 54696
33.3%
4 27348
16.7%
. 27348
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 54696
33.3%
5 54696
33.3%
4 27348
16.7%
. 27348
16.7%

AP3_2_Actual_Part_to_Service
Real number (ℝ)

High correlation 

Distinct14841
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6021.1746
Minimum0
Maximum14999
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size427.3 KiB
2025-03-07T16:24:44.103661image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile463.35
Q12324.75
median4665
Q39638.25
95-th percentile14301
Maximum14999
Range14999
Interquartile range (IQR)7313.5

Descriptive statistics

Standard deviation4505.5303
Coefficient of variation (CV)0.74828096
Kurtosis-0.93634557
Mean6021.1746
Median Absolute Deviation (MAD)2994
Skewness0.59760962
Sum1.6466708 × 108
Variance20299804
MonotonicityNot monotonic
2025-03-07T16:24:44.271024image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1606 3
 
< 0.1%
1502 3
 
< 0.1%
1508 3
 
< 0.1%
1507 3
 
< 0.1%
1506 3
 
< 0.1%
1505 3
 
< 0.1%
1504 3
 
< 0.1%
1503 3
 
< 0.1%
1501 3
 
< 0.1%
1510 3
 
< 0.1%
Other values (14831) 27318
99.9%
ValueCountFrequency (%)
0 3
< 0.1%
1 3
< 0.1%
2 3
< 0.1%
3 2
< 0.1%
4 3
< 0.1%
5 3
< 0.1%
6 3
< 0.1%
7 3
< 0.1%
8 3
< 0.1%
9 2
< 0.1%
ValueCountFrequency (%)
14999 2
< 0.1%
14998 2
< 0.1%
14997 2
< 0.1%
14996 2
< 0.1%
14995 2
< 0.1%
14994 2
< 0.1%
14993 2
< 0.1%
14992 2
< 0.1%
14991 2
< 0.1%
14990 2
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size427.3 KiB
1.9
27348 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters82,044
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.9
2nd row1.9
3rd row1.9
4th row1.9
5th row1.9

Common Values

ValueCountFrequency (%)
1.9 27348
100.0%

Length

2025-03-07T16:24:44.391899image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-07T16:24:44.529824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1.9 27348
100.0%

Most occurring characters

ValueCountFrequency (%)
1 27348
33.3%
. 27348
33.3%
9 27348
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54696
66.7%
Other Punctuation 27348
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 27348
50.0%
9 27348
50.0%
Other Punctuation
ValueCountFrequency (%)
. 27348
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 82044
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 27348
33.3%
. 27348
33.3%
9 27348
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82044
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 27348
33.3%
. 27348
33.3%
9 27348
33.3%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size427.3 KiB
-17.05
27348 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters164,088
Distinct characters6
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-17.05
2nd row-17.05
3rd row-17.05
4th row-17.05
5th row-17.05

Common Values

ValueCountFrequency (%)
-17.05 27348
100.0%

Length

2025-03-07T16:24:44.620449image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-07T16:24:44.706012image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
17.05 27348
100.0%

Most occurring characters

ValueCountFrequency (%)
- 27348
16.7%
1 27348
16.7%
7 27348
16.7%
. 27348
16.7%
0 27348
16.7%
5 27348
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 109392
66.7%
Dash Punctuation 27348
 
16.7%
Other Punctuation 27348
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 27348
25.0%
7 27348
25.0%
0 27348
25.0%
5 27348
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 27348
100.0%
Other Punctuation
ValueCountFrequency (%)
. 27348
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 164088
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 27348
16.7%
1 27348
16.7%
7 27348
16.7%
. 27348
16.7%
0 27348
16.7%
5 27348
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 164088
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 27348
16.7%
1 27348
16.7%
7 27348
16.7%
. 27348
16.7%
0 27348
16.7%
5 27348
16.7%

PARAM_InclinationBeltDirectionOffset__deg_
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size427.3 KiB
0.01
12137 
-0.08
5977 
-0.05
4907 
-0.03
4327 

Length

Max length5
Median length5
Mean length4.5562016
Min length4

Characters and Unicode

Total characters124,603
Distinct characters7
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.05
2nd row-0.05
3rd row-0.05
4th row-0.05
5th row-0.05

Common Values

ValueCountFrequency (%)
0.01 12137
44.4%
-0.08 5977
21.9%
-0.05 4907
17.9%
-0.03 4327
 
15.8%

Length

2025-03-07T16:24:44.807167image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-07T16:24:44.923697image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.01 12137
44.4%
0.08 5977
21.9%
0.05 4907
17.9%
0.03 4327
 
15.8%

Most occurring characters

ValueCountFrequency (%)
0 54696
43.9%
. 27348
21.9%
- 15211
 
12.2%
1 12137
 
9.7%
8 5977
 
4.8%
5 4907
 
3.9%
3 4327
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82044
65.8%
Other Punctuation 27348
 
21.9%
Dash Punctuation 15211
 
12.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54696
66.7%
1 12137
 
14.8%
8 5977
 
7.3%
5 4907
 
6.0%
3 4327
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 27348
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15211
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 124603
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 54696
43.9%
. 27348
21.9%
- 15211
 
12.2%
1 12137
 
9.7%
8 5977
 
4.8%
5 4907
 
3.9%
3 4327
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 54696
43.9%
. 27348
21.9%
- 15211
 
12.2%
1 12137
 
9.7%
8 5977
 
4.8%
5 4907
 
3.9%
3 4327
 
3.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size427.3 KiB
0.37
12137 
0.62
5977 
0.5
4907 
0.48
4327 

Length

Max length4
Median length4
Mean length3.8205719
Min length3

Characters and Unicode

Total characters104,485
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.37 12137
44.4%
0.62 5977
21.9%
0.5 4907
17.9%
0.48 4327
 
15.8%

Length

2025-03-07T16:24:45.080737image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-07T16:24:45.203100image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.37 12137
44.4%
0.62 5977
21.9%
0.5 4907
17.9%
0.48 4327
 
15.8%

Most occurring characters

ValueCountFrequency (%)
0 27348
26.2%
. 27348
26.2%
3 12137
11.6%
7 12137
11.6%
6 5977
 
5.7%
2 5977
 
5.7%
5 4907
 
4.7%
4 4327
 
4.1%
8 4327
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77137
73.8%
Other Punctuation 27348
 
26.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27348
35.5%
3 12137
15.7%
7 12137
15.7%
6 5977
 
7.7%
2 5977
 
7.7%
5 4907
 
6.4%
4 4327
 
5.6%
8 4327
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 27348
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 104485
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27348
26.2%
. 27348
26.2%
3 12137
11.6%
7 12137
11.6%
6 5977
 
5.7%
2 5977
 
5.7%
5 4907
 
4.7%
4 4327
 
4.1%
8 4327
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27348
26.2%
. 27348
26.2%
3 12137
11.6%
7 12137
11.6%
6 5977
 
5.7%
2 5977
 
5.7%
5 4907
 
4.7%
4 4327
 
4.1%
8 4327
 
4.1%

PARAM_M826FirstPosition__mm_
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size427.3 KiB
-3.0
16464 
2.0
10884 

Length

Max length4
Median length4
Mean length3.6020184
Min length3

Characters and Unicode

Total characters98,508
Distinct characters5
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
-3.0 16464
60.2%
2.0 10884
39.8%

Length

2025-03-07T16:24:45.323057image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-07T16:24:45.417976image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3.0 16464
60.2%
2.0 10884
39.8%

Most occurring characters

ValueCountFrequency (%)
. 27348
27.8%
0 27348
27.8%
- 16464
16.7%
3 16464
16.7%
2 10884
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54696
55.5%
Other Punctuation 27348
27.8%
Dash Punctuation 16464
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27348
50.0%
3 16464
30.1%
2 10884
 
19.9%
Other Punctuation
ValueCountFrequency (%)
. 27348
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16464
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 98508
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 27348
27.8%
0 27348
27.8%
- 16464
16.7%
3 16464
16.7%
2 10884
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 27348
27.8%
0 27348
27.8%
- 16464
16.7%
3 16464
16.7%
2 10884
 
11.0%

PARAM_M826SecondPosition__mm_
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size427.3 KiB
-13.5
16464 
-8.5
10884 

Length

Max length5
Median length5
Mean length4.6020184
Min length4

Characters and Unicode

Total characters125,856
Distinct characters6
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-8.5
2nd row-8.5
3rd row-8.5
4th row-8.5
5th row-8.5

Common Values

ValueCountFrequency (%)
-13.5 16464
60.2%
-8.5 10884
39.8%

Length

2025-03-07T16:24:45.538033image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-07T16:24:45.626403image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
13.5 16464
60.2%
8.5 10884
39.8%

Most occurring characters

ValueCountFrequency (%)
- 27348
21.7%
. 27348
21.7%
5 27348
21.7%
1 16464
13.1%
3 16464
13.1%
8 10884
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71160
56.5%
Dash Punctuation 27348
 
21.7%
Other Punctuation 27348
 
21.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 27348
38.4%
1 16464
23.1%
3 16464
23.1%
8 10884
 
15.3%
Dash Punctuation
ValueCountFrequency (%)
- 27348
100.0%
Other Punctuation
ValueCountFrequency (%)
. 27348
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 125856
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 27348
21.7%
. 27348
21.7%
5 27348
21.7%
1 16464
13.1%
3 16464
13.1%
8 10884
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 27348
21.7%
. 27348
21.7%
5 27348
21.7%
1 16464
13.1%
3 16464
13.1%
8 10884
 
8.6%

PARAM_M826InsertLockPinPosition__mm_
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size427.3 KiB
-13.5
16464 
-8.5
10884 

Length

Max length5
Median length5
Mean length4.6020184
Min length4

Characters and Unicode

Total characters125,856
Distinct characters6
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-8.5
2nd row-8.5
3rd row-8.5
4th row-8.5
5th row-8.5

Common Values

ValueCountFrequency (%)
-13.5 16464
60.2%
-8.5 10884
39.8%

Length

2025-03-07T16:24:45.727270image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-07T16:24:45.821874image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
13.5 16464
60.2%
8.5 10884
39.8%

Most occurring characters

ValueCountFrequency (%)
- 27348
21.7%
. 27348
21.7%
5 27348
21.7%
1 16464
13.1%
3 16464
13.1%
8 10884
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71160
56.5%
Dash Punctuation 27348
 
21.7%
Other Punctuation 27348
 
21.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 27348
38.4%
1 16464
23.1%
3 16464
23.1%
8 10884
 
15.3%
Dash Punctuation
ValueCountFrequency (%)
- 27348
100.0%
Other Punctuation
ValueCountFrequency (%)
. 27348
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 125856
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 27348
21.7%
. 27348
21.7%
5 27348
21.7%
1 16464
13.1%
3 16464
13.1%
8 10884
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 27348
21.7%
. 27348
21.7%
5 27348
21.7%
1 16464
13.1%
3 16464
13.1%
8 10884
 
8.6%

RESULT_XWheelUpAverageSensor1__mm_
Real number (ℝ)

High correlation  Zeros 

Distinct77
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16833333
Minimum-0.22
Maximum0.59
Zeros306
Zeros (%)1.1%
Negative770
Negative (%)2.8%
Memory size427.3 KiB
2025-03-07T16:24:45.930368image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.22
5-th percentile0.02
Q10.11
median0.17
Q30.23
95-th percentile0.33
Maximum0.59
Range0.81
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.094290016
Coefficient of variation (CV)0.56013871
Kurtosis0.29645841
Mean0.16833333
Median Absolute Deviation (MAD)0.06
Skewness0.15213771
Sum4603.58
Variance0.0088906071
MonotonicityNot monotonic
2025-03-07T16:24:46.067289image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.16 1326
 
4.8%
0.18 1208
 
4.4%
0.17 1194
 
4.4%
0.15 1189
 
4.3%
0.14 1179
 
4.3%
0.13 1172
 
4.3%
0.19 1108
 
4.1%
0.12 1077
 
3.9%
0.2 1060
 
3.9%
0.21 1042
 
3.8%
Other values (67) 15793
57.7%
ValueCountFrequency (%)
-0.22 2
 
< 0.1%
-0.2 1
 
< 0.1%
-0.17 3
 
< 0.1%
-0.16 4
 
< 0.1%
-0.15 5
 
< 0.1%
-0.14 7
 
< 0.1%
-0.13 9
< 0.1%
-0.12 12
< 0.1%
-0.11 14
0.1%
-0.1 22
0.1%
ValueCountFrequency (%)
0.59 1
 
< 0.1%
0.56 2
 
< 0.1%
0.55 2
 
< 0.1%
0.54 2
 
< 0.1%
0.53 2
 
< 0.1%
0.52 5
 
< 0.1%
0.51 5
 
< 0.1%
0.5 5
 
< 0.1%
0.49 13
< 0.1%
0.48 7
< 0.1%

RESULT_XWheelUpAverageSensor2__mm_
Real number (ℝ)

High correlation 

Distinct65
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37468956
Minimum0
Maximum0.7
Zeros130
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size427.3 KiB
2025-03-07T16:24:46.199448image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.32
median0.38
Q30.43
95-th percentile0.5
Maximum0.7
Range0.7
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.081353657
Coefficient of variation (CV)0.21712283
Kurtosis1.6039076
Mean0.37468956
Median Absolute Deviation (MAD)0.05
Skewness-0.35636699
Sum10247.01
Variance0.0066184175
MonotonicityNot monotonic
2025-03-07T16:24:46.331584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.38 1406
 
5.1%
0.39 1362
 
5.0%
0.37 1347
 
4.9%
0.4 1316
 
4.8%
0.36 1316
 
4.8%
0.35 1304
 
4.8%
0.34 1257
 
4.6%
0.33 1249
 
4.6%
0.42 1246
 
4.6%
0.41 1222
 
4.5%
Other values (55) 14323
52.4%
ValueCountFrequency (%)
0 130
0.5%
0.07 1
 
< 0.1%
0.08 2
 
< 0.1%
0.09 2
 
< 0.1%
0.1 2
 
< 0.1%
0.11 6
 
< 0.1%
0.12 4
 
< 0.1%
0.13 9
 
< 0.1%
0.14 15
 
0.1%
0.15 19
 
0.1%
ValueCountFrequency (%)
0.7 2
 
< 0.1%
0.69 2
 
< 0.1%
0.68 1
 
< 0.1%
0.67 2
 
< 0.1%
0.66 1
 
< 0.1%
0.65 8
 
< 0.1%
0.64 4
 
< 0.1%
0.63 5
 
< 0.1%
0.62 13
< 0.1%
0.61 20
0.1%

RESULT_XWheelUpAverageDiff__mm_
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.20636792
Minimum-0.38
Maximum0
Zeros130
Zeros (%)0.5%
Negative27218
Negative (%)99.5%
Memory size427.3 KiB
2025-03-07T16:24:46.522553image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.38
5-th percentile-0.3
Q1-0.24
median-0.2
Q3-0.17
95-th percentile-0.13
Maximum0
Range0.38
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.052665495
Coefficient of variation (CV)-0.25520194
Kurtosis0.2589277
Mean-0.20636792
Median Absolute Deviation (MAD)0.04
Skewness-0.031706633
Sum-5643.75
Variance0.0027736544
MonotonicityNot monotonic
2025-03-07T16:24:46.670712image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
-0.19 1905
 
7.0%
-0.17 1884
 
6.9%
-0.16 1865
 
6.8%
-0.21 1845
 
6.7%
-0.18 1810
 
6.6%
-0.2 1801
 
6.6%
-0.22 1750
 
6.4%
-0.23 1685
 
6.2%
-0.15 1585
 
5.8%
-0.24 1545
 
5.6%
Other values (23) 9673
35.4%
ValueCountFrequency (%)
-0.38 2
 
< 0.1%
-0.37 7
 
< 0.1%
-0.36 20
 
0.1%
-0.35 30
 
0.1%
-0.34 80
 
0.3%
-0.33 146
 
0.5%
-0.32 239
 
0.9%
-0.31 377
1.4%
-0.3 517
1.9%
-0.29 685
2.5%
ValueCountFrequency (%)
0 130
 
0.5%
-0.07 1
 
< 0.1%
-0.08 3
 
< 0.1%
-0.09 23
 
0.1%
-0.1 78
 
0.3%
-0.11 213
 
0.8%
-0.12 451
 
1.6%
-0.13 869
3.2%
-0.14 1316
4.8%
-0.15 1585
5.8%

RESULT_XWheelUpAngle__deg_
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.58681915
Minimum-1.08
Maximum0
Zeros130
Zeros (%)0.5%
Negative27218
Negative (%)99.5%
Memory size427.3 KiB
2025-03-07T16:24:46.808771image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1.08
5-th percentile-0.84
Q1-0.69
median-0.58
Q3-0.47
95-th percentile-0.37
Maximum0
Range1.08
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.1495985
Coefficient of variation (CV)-0.25493119
Kurtosis0.25948335
Mean-0.58681915
Median Absolute Deviation (MAD)0.11
Skewness-0.030970513
Sum-16048.33
Variance0.022379712
MonotonicityNot monotonic
2025-03-07T16:24:46.947399image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.54 686
 
2.5%
-0.46 686
 
2.5%
-0.48 684
 
2.5%
-0.53 666
 
2.4%
-0.56 661
 
2.4%
-0.63 655
 
2.4%
-0.6 655
 
2.4%
-0.55 652
 
2.4%
-0.45 652
 
2.4%
-0.49 648
 
2.4%
Other values (78) 20703
75.7%
ValueCountFrequency (%)
-1.08 1
 
< 0.1%
-1.07 1
 
< 0.1%
-1.06 2
 
< 0.1%
-1.05 3
 
< 0.1%
-1.04 2
 
< 0.1%
-1.03 4
 
< 0.1%
-1.02 7
< 0.1%
-1.01 13
< 0.1%
-1 12
< 0.1%
-0.99 7
< 0.1%
ValueCountFrequency (%)
0 130
0.5%
-0.2 1
 
< 0.1%
-0.23 1
 
< 0.1%
-0.24 2
 
< 0.1%
-0.25 7
 
< 0.1%
-0.26 11
 
< 0.1%
-0.27 12
 
< 0.1%
-0.28 22
 
0.1%
-0.29 32
 
0.1%
-0.3 50
 
0.2%

RESULT_XWheelDownAverageSensor1__mm_
Real number (ℝ)

High correlation  Zeros 

Distinct76
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11719614
Minimum-0.26
Maximum0.54
Zeros640
Zeros (%)2.3%
Negative2302
Negative (%)8.4%
Memory size427.3 KiB
2025-03-07T16:24:47.094524image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.26
5-th percentile-0.03
Q10.06
median0.11
Q30.18
95-th percentile0.28
Maximum0.54
Range0.8
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.092883777
Coefficient of variation (CV)0.79254981
Kurtosis0.28600555
Mean0.11719614
Median Absolute Deviation (MAD)0.06
Skewness0.1631792
Sum3205.08
Variance0.0086273961
MonotonicityNot monotonic
2025-03-07T16:24:47.249113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11 1259
 
4.6%
0.12 1253
 
4.6%
0.1 1218
 
4.5%
0.13 1211
 
4.4%
0.14 1190
 
4.4%
0.08 1189
 
4.3%
0.09 1165
 
4.3%
0.07 1075
 
3.9%
0.15 1073
 
3.9%
0.16 1054
 
3.9%
Other values (66) 15661
57.3%
ValueCountFrequency (%)
-0.26 1
 
< 0.1%
-0.25 2
 
< 0.1%
-0.21 4
 
< 0.1%
-0.2 7
 
< 0.1%
-0.19 3
 
< 0.1%
-0.18 12
< 0.1%
-0.17 6
 
< 0.1%
-0.16 19
0.1%
-0.15 17
0.1%
-0.14 20
0.1%
ValueCountFrequency (%)
0.54 1
 
< 0.1%
0.52 1
 
< 0.1%
0.5 3
 
< 0.1%
0.49 1
 
< 0.1%
0.48 1
 
< 0.1%
0.47 3
 
< 0.1%
0.46 6
< 0.1%
0.45 5
< 0.1%
0.44 11
< 0.1%
0.43 8
< 0.1%

RESULT_XWheelDownAverageSensor2__mm_
Real number (ℝ)

High correlation 

Distinct64
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37441934
Minimum0
Maximum0.7
Zeros130
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size427.3 KiB
2025-03-07T16:24:47.425378image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.32
median0.38
Q30.43
95-th percentile0.5
Maximum0.7
Range0.7
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.081914793
Coefficient of variation (CV)0.21877821
Kurtosis1.520497
Mean0.37441934
Median Absolute Deviation (MAD)0.05
Skewness-0.36039289
Sum10239.62
Variance0.0067100332
MonotonicityNot monotonic
2025-03-07T16:24:47.595733image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.37 1397
 
5.1%
0.39 1388
 
5.1%
0.35 1321
 
4.8%
0.4 1320
 
4.8%
0.38 1302
 
4.8%
0.36 1277
 
4.7%
0.34 1271
 
4.6%
0.33 1224
 
4.5%
0.41 1218
 
4.5%
0.42 1187
 
4.3%
Other values (54) 14443
52.8%
ValueCountFrequency (%)
0 130
0.5%
0.07 1
 
< 0.1%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 3
 
< 0.1%
0.11 7
 
< 0.1%
0.12 8
 
< 0.1%
0.13 10
 
< 0.1%
0.14 14
 
0.1%
0.15 12
 
< 0.1%
ValueCountFrequency (%)
0.7 1
 
< 0.1%
0.69 4
 
< 0.1%
0.67 3
 
< 0.1%
0.66 2
 
< 0.1%
0.65 5
 
< 0.1%
0.64 4
 
< 0.1%
0.63 5
 
< 0.1%
0.62 13
< 0.1%
0.61 13
< 0.1%
0.6 22
0.1%

RESULT_XWheelDownAverageDiff__mm_
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.25717493
Minimum-0.43
Maximum0
Zeros130
Zeros (%)0.5%
Negative27218
Negative (%)99.5%
Memory size427.3 KiB
2025-03-07T16:24:47.722868image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.43
5-th percentile-0.35
Q1-0.29
median-0.25
Q3-0.22
95-th percentile-0.18
Maximum0
Range0.43
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.053138571
Coefficient of variation (CV)-0.20662423
Kurtosis1.5888941
Mean-0.25717493
Median Absolute Deviation (MAD)0.04
Skewness0.23858269
Sum-7033.22
Variance0.0028237077
MonotonicityNot monotonic
2025-03-07T16:24:49.097197image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
-0.25 1980
 
7.2%
-0.24 1876
 
6.9%
-0.23 1853
 
6.8%
-0.22 1852
 
6.8%
-0.26 1816
 
6.6%
-0.28 1800
 
6.6%
-0.27 1790
 
6.5%
-0.21 1704
 
6.2%
-0.29 1610
 
5.9%
-0.2 1595
 
5.8%
Other values (22) 9472
34.6%
ValueCountFrequency (%)
-0.43 2
 
< 0.1%
-0.42 2
 
< 0.1%
-0.41 17
 
0.1%
-0.4 36
 
0.1%
-0.39 86
 
0.3%
-0.38 140
 
0.5%
-0.37 249
 
0.9%
-0.36 339
1.2%
-0.35 554
2.0%
-0.34 675
2.5%
ValueCountFrequency (%)
0 130
 
0.5%
-0.13 2
 
< 0.1%
-0.14 14
 
0.1%
-0.15 66
 
0.2%
-0.16 182
 
0.7%
-0.17 433
 
1.6%
-0.18 812
3.0%
-0.19 1236
4.5%
-0.2 1595
5.8%
-0.21 1704
6.2%

RESULT_XWheelDownAngle__deg_
Real number (ℝ)

High correlation 

Distinct85
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.73122276
Minimum-1.22
Maximum0
Zeros130
Zeros (%)0.5%
Negative27218
Negative (%)99.5%
Memory size427.3 KiB
2025-03-07T16:24:49.223708image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1.22
5-th percentile-0.98
Q1-0.83
median-0.72
Q3-0.62
95-th percentile-0.52
Maximum0
Range1.22
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.15094593
Coefficient of variation (CV)-0.20642948
Kurtosis1.5970626
Mean-0.73122276
Median Absolute Deviation (MAD)0.11
Skewness0.23886884
Sum-19997.48
Variance0.022784675
MonotonicityNot monotonic
2025-03-07T16:24:49.356803image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7 727
 
2.7%
-0.71 702
 
2.6%
-0.72 680
 
2.5%
-0.74 671
 
2.5%
-0.62 665
 
2.4%
-0.68 661
 
2.4%
-0.63 655
 
2.4%
-0.66 653
 
2.4%
-0.76 652
 
2.4%
-0.64 647
 
2.4%
Other values (75) 20635
75.5%
ValueCountFrequency (%)
-1.22 1
 
< 0.1%
-1.21 1
 
< 0.1%
-1.19 2
 
< 0.1%
-1.18 2
 
< 0.1%
-1.17 7
 
< 0.1%
-1.16 7
 
< 0.1%
-1.15 8
 
< 0.1%
-1.14 15
0.1%
-1.13 11
 
< 0.1%
-1.12 28
0.1%
ValueCountFrequency (%)
0 130
0.5%
-0.37 2
 
< 0.1%
-0.39 5
 
< 0.1%
-0.4 2
 
< 0.1%
-0.41 11
 
< 0.1%
-0.42 27
 
0.1%
-0.43 20
 
0.1%
-0.44 28
 
0.1%
-0.45 63
0.2%
-0.46 64
0.2%

RESULT_XAngle__deg_
Real number (ℝ)

High correlation 

Distinct87
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.65901638
Minimum-1.15
Maximum0
Zeros130
Zeros (%)0.5%
Negative27218
Negative (%)99.5%
Memory size427.3 KiB
2025-03-07T16:24:49.523108image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1.15
5-th percentile-0.91
Q1-0.76
median-0.65
Q3-0.55
95-th percentile-0.45
Maximum0
Range1.15
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.14984107
Coefficient of variation (CV)-0.22737079
Kurtosis0.83400922
Mean-0.65901638
Median Absolute Deviation (MAD)0.11
Skewness0.092849908
Sum-18022.78
Variance0.022452347
MonotonicityNot monotonic
2025-03-07T16:24:49.658446image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.63 691
 
2.5%
-0.62 672
 
2.5%
-0.64 670
 
2.4%
-0.58 669
 
2.4%
-0.61 663
 
2.4%
-0.67 654
 
2.4%
-0.52 653
 
2.4%
-0.69 653
 
2.4%
-0.55 652
 
2.4%
-0.56 651
 
2.4%
Other values (77) 20720
75.8%
ValueCountFrequency (%)
-1.15 1
 
< 0.1%
-1.13 2
 
< 0.1%
-1.12 1
 
< 0.1%
-1.11 4
 
< 0.1%
-1.1 3
 
< 0.1%
-1.09 10
< 0.1%
-1.08 7
 
< 0.1%
-1.07 10
< 0.1%
-1.06 19
0.1%
-1.05 16
0.1%
ValueCountFrequency (%)
0 130
0.5%
-0.29 1
 
< 0.1%
-0.3 1
 
< 0.1%
-0.31 1
 
< 0.1%
-0.32 3
 
< 0.1%
-0.33 6
 
< 0.1%
-0.34 14
 
0.1%
-0.35 22
 
0.1%
-0.36 22
 
0.1%
-0.37 41
 
0.1%

RESULT_XAngleMax__deg_
Real number (ℝ)

High correlation 

Distinct85
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.73122276
Minimum-1.22
Maximum0
Zeros130
Zeros (%)0.5%
Negative27218
Negative (%)99.5%
Memory size427.3 KiB
2025-03-07T16:24:49.794367image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-1.22
5-th percentile-0.98
Q1-0.83
median-0.72
Q3-0.62
95-th percentile-0.52
Maximum0
Range1.22
Interquartile range (IQR)0.21

Descriptive statistics

Standard deviation0.15094593
Coefficient of variation (CV)-0.20642948
Kurtosis1.5970626
Mean-0.73122276
Median Absolute Deviation (MAD)0.11
Skewness0.23886884
Sum-19997.48
Variance0.022784675
MonotonicityNot monotonic
2025-03-07T16:24:49.927648image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7 727
 
2.7%
-0.71 702
 
2.6%
-0.72 680
 
2.5%
-0.74 671
 
2.5%
-0.62 665
 
2.4%
-0.68 661
 
2.4%
-0.63 655
 
2.4%
-0.66 653
 
2.4%
-0.76 652
 
2.4%
-0.64 647
 
2.4%
Other values (75) 20635
75.5%
ValueCountFrequency (%)
-1.22 1
 
< 0.1%
-1.21 1
 
< 0.1%
-1.19 2
 
< 0.1%
-1.18 2
 
< 0.1%
-1.17 7
 
< 0.1%
-1.16 7
 
< 0.1%
-1.15 8
 
< 0.1%
-1.14 15
0.1%
-1.13 11
 
< 0.1%
-1.12 28
0.1%
ValueCountFrequency (%)
0 130
0.5%
-0.37 2
 
< 0.1%
-0.39 5
 
< 0.1%
-0.4 2
 
< 0.1%
-0.41 11
 
< 0.1%
-0.42 27
 
0.1%
-0.43 20
 
0.1%
-0.44 28
 
0.1%
-0.45 63
0.2%
-0.46 64
0.2%

RESULT_ZWheelUpAverageSensor3__mm_
Real number (ℝ)

High correlation 

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.43611672
Minimum-0.58
Maximum0
Zeros130
Zeros (%)0.5%
Negative27218
Negative (%)99.5%
Memory size427.3 KiB
2025-03-07T16:24:50.068767image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.58
5-th percentile-0.54
Q1-0.52
median-0.42
Q3-0.37
95-th percentile-0.35
Maximum0
Range0.58
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.079245536
Coefficient of variation (CV)-0.18170717
Kurtosis2.4001881
Mean-0.43611672
Median Absolute Deviation (MAD)0.07
Skewness0.57512292
Sum-11926.92
Variance0.006279855
MonotonicityNot monotonic
2025-03-07T16:24:50.226422image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
-0.36 2987
10.9%
-0.37 2952
10.8%
-0.52 2535
 
9.3%
-0.53 2222
 
8.1%
-0.51 2155
 
7.9%
-0.38 2106
 
7.7%
-0.35 1948
 
7.1%
-0.54 1461
 
5.3%
-0.43 1322
 
4.8%
-0.5 1094
 
4.0%
Other values (25) 6566
24.0%
ValueCountFrequency (%)
-0.58 1
 
< 0.1%
-0.57 34
 
0.1%
-0.56 215
 
0.8%
-0.55 731
 
2.7%
-0.54 1461
5.3%
-0.53 2222
8.1%
-0.52 2535
9.3%
-0.51 2155
7.9%
-0.5 1094
4.0%
-0.49 296
 
1.1%
ValueCountFrequency (%)
0 130
0.5%
-0.25 1
 
< 0.1%
-0.26 2
 
< 0.1%
-0.27 1
 
< 0.1%
-0.28 2
 
< 0.1%
-0.29 6
 
< 0.1%
-0.3 4
 
< 0.1%
-0.31 6
 
< 0.1%
-0.32 34
 
0.1%
-0.33 160
0.6%

RESULT_ZWheelUpAverageSensor4__mm_
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.5611361
Minimum-0.74
Maximum0
Zeros130
Zeros (%)0.5%
Negative27218
Negative (%)99.5%
Memory size427.3 KiB
2025-03-07T16:24:50.343592image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.74
5-th percentile-0.69
Q1-0.67
median-0.55
Q3-0.47
95-th percentile-0.44
Maximum0
Range0.74
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.10321376
Coefficient of variation (CV)-0.18393713
Kurtosis2.1820321
Mean-0.5611361
Median Absolute Deviation (MAD)0.09
Skewness0.59293735
Sum-15345.95
Variance0.010653081
MonotonicityNot monotonic
2025-03-07T16:24:50.469893image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
-0.67 2372
 
8.7%
-0.68 2220
 
8.1%
-0.47 2175
 
8.0%
-0.48 1940
 
7.1%
-0.46 1896
 
6.9%
-0.66 1892
 
6.9%
-0.69 1494
 
5.5%
-0.45 1461
 
5.3%
-0.49 1450
 
5.3%
-0.65 1049
 
3.8%
Other values (30) 9399
34.4%
ValueCountFrequency (%)
-0.74 5
 
< 0.1%
-0.73 16
 
0.1%
-0.72 103
 
0.4%
-0.71 326
 
1.2%
-0.7 754
 
2.8%
-0.69 1494
5.5%
-0.68 2220
8.1%
-0.67 2372
8.7%
-0.66 1892
6.9%
-0.65 1049
3.8%
ValueCountFrequency (%)
0 130
 
0.5%
-0.33 1
 
< 0.1%
-0.35 1
 
< 0.1%
-0.38 2
 
< 0.1%
-0.39 5
 
< 0.1%
-0.4 22
 
0.1%
-0.41 90
 
0.3%
-0.42 226
 
0.8%
-0.43 486
1.8%
-0.44 914
3.3%

RESULT_ZWheelUpAverageDiff__mm_
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12504534
Minimum0
Maximum0.23
Zeros131
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size427.3 KiB
2025-03-07T16:24:50.587126image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.08
Q10.1
median0.12
Q30.15
95-th percentile0.18
Maximum0.23
Range0.23
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.031358279
Coefficient of variation (CV)0.25077526
Kurtosis0.48805705
Mean0.12504534
Median Absolute Deviation (MAD)0.02
Skewness-0.13725432
Sum3419.74
Variance0.00098334164
MonotonicityNot monotonic
2025-03-07T16:24:50.716481image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.12 3335
12.2%
0.13 3201
11.7%
0.11 3164
11.6%
0.14 2893
10.6%
0.1 2865
10.5%
0.15 2592
9.5%
0.09 2121
7.8%
0.16 1910
7.0%
0.17 1343
4.9%
0.08 1280
 
4.7%
Other values (14) 2644
9.7%
ValueCountFrequency (%)
0 131
 
0.5%
0.01 1
 
< 0.1%
0.02 1
 
< 0.1%
0.03 5
 
< 0.1%
0.04 20
 
0.1%
0.05 96
 
0.4%
0.06 271
 
1.0%
0.07 600
 
2.2%
0.08 1280
4.7%
0.09 2121
7.8%
ValueCountFrequency (%)
0.23 3
 
< 0.1%
0.22 21
 
0.1%
0.21 66
 
0.2%
0.2 159
 
0.6%
0.19 424
 
1.6%
0.18 846
 
3.1%
0.17 1343
4.9%
0.16 1910
7.0%
0.15 2592
9.5%
0.14 2893
10.6%

RESULT_ZWheelUpAngle__deg_
Real number (ℝ)

High correlation 

Distinct63
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35559456
Minimum0
Maximum0.65
Zeros130
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size427.3 KiB
2025-03-07T16:24:50.840022image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.22
Q10.29
median0.35
Q30.42
95-th percentile0.5
Maximum0.65
Range0.65
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.088722571
Coefficient of variation (CV)0.24950486
Kurtosis0.49945768
Mean0.35559456
Median Absolute Deviation (MAD)0.06
Skewness-0.13751568
Sum9724.8
Variance0.0078716946
MonotonicityNot monotonic
2025-03-07T16:24:50.969396image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.35 1181
 
4.3%
0.32 1166
 
4.3%
0.34 1166
 
4.3%
0.37 1153
 
4.2%
0.33 1145
 
4.2%
0.36 1138
 
4.2%
0.38 1085
 
4.0%
0.31 1082
 
4.0%
0.3 1079
 
3.9%
0.39 1061
 
3.9%
Other values (53) 16092
58.8%
ValueCountFrequency (%)
0 130
0.5%
0.01 1
 
< 0.1%
0.04 1
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 2
 
< 0.1%
0.1 2
 
< 0.1%
0.11 4
 
< 0.1%
0.12 10
 
< 0.1%
ValueCountFrequency (%)
0.65 3
 
< 0.1%
0.64 1
 
< 0.1%
0.63 4
 
< 0.1%
0.62 11
 
< 0.1%
0.61 12
 
< 0.1%
0.6 16
 
0.1%
0.59 35
0.1%
0.58 31
 
0.1%
0.57 54
0.2%
0.56 78
0.3%

RESULT_ZWheelDownAverageSensor3__mm_
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.47679611
Minimum-0.62
Maximum0
Zeros130
Zeros (%)0.5%
Negative27218
Negative (%)99.5%
Memory size427.3 KiB
2025-03-07T16:24:51.147408image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.62
5-th percentile-0.58
Q1-0.56
median-0.46
Q3-0.41
95-th percentile-0.39
Maximum0
Range0.62
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.081606864
Coefficient of variation (CV)-0.17115673
Kurtosis3.5378191
Mean-0.47679611
Median Absolute Deviation (MAD)0.06
Skewness0.72186549
Sum-13039.42
Variance0.0066596802
MonotonicityNot monotonic
2025-03-07T16:24:51.265115image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
-0.41 2797
 
10.2%
-0.4 2695
 
9.9%
-0.57 2522
 
9.2%
-0.56 2464
 
9.0%
-0.42 2266
 
8.3%
-0.58 1811
 
6.6%
-0.55 1701
 
6.2%
-0.39 1677
 
6.1%
-0.43 1185
 
4.3%
-0.47 1020
 
3.7%
Other values (24) 7210
26.4%
ValueCountFrequency (%)
-0.62 12
 
< 0.1%
-0.61 58
 
0.2%
-0.6 298
 
1.1%
-0.59 850
 
3.1%
-0.58 1811
6.6%
-0.57 2522
9.2%
-0.56 2464
9.0%
-0.55 1701
6.2%
-0.54 731
 
2.7%
-0.53 264
 
1.0%
ValueCountFrequency (%)
0 130
 
0.5%
-0.29 1
 
< 0.1%
-0.3 1
 
< 0.1%
-0.32 1
 
< 0.1%
-0.33 5
 
< 0.1%
-0.34 2
 
< 0.1%
-0.35 10
 
< 0.1%
-0.36 64
 
0.2%
-0.37 278
 
1.0%
-0.38 789
2.9%

RESULT_ZWheelDownAverageSensor4__mm_
Real number (ℝ)

High correlation 

Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.42160706
Minimum-0.58
Maximum0
Zeros130
Zeros (%)0.5%
Negative27218
Negative (%)99.5%
Memory size427.3 KiB
2025-03-07T16:24:51.379071image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.58
5-th percentile-0.54
Q1-0.52
median-0.41
Q3-0.34
95-th percentile-0.31
Maximum0
Range0.58
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.0941672
Coefficient of variation (CV)-0.22335299
Kurtosis0.014258446
Mean-0.42160706
Median Absolute Deviation (MAD)0.09
Skewness0.22358363
Sum-11530.11
Variance0.0088674615
MonotonicityNot monotonic
2025-03-07T16:24:51.537916image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
-0.33 2906
10.6%
-0.34 2873
10.5%
-0.53 2704
 
9.9%
-0.52 2501
 
9.1%
-0.54 1929
 
7.1%
-0.32 1908
 
7.0%
-0.35 1881
 
6.9%
-0.51 1574
 
5.8%
-0.41 1148
 
4.2%
-0.42 1090
 
4.0%
Other values (26) 6834
25.0%
ValueCountFrequency (%)
-0.58 3
 
< 0.1%
-0.57 69
 
0.3%
-0.56 338
 
1.2%
-0.55 922
 
3.4%
-0.54 1929
7.1%
-0.53 2704
9.9%
-0.52 2501
9.1%
-0.51 1574
5.8%
-0.5 597
 
2.2%
-0.49 136
 
0.5%
ValueCountFrequency (%)
0 130
 
0.5%
-0.15 1
 
< 0.1%
-0.24 1
 
< 0.1%
-0.26 1
 
< 0.1%
-0.27 3
 
< 0.1%
-0.28 13
 
< 0.1%
-0.29 88
 
0.3%
-0.3 298
 
1.1%
-0.31 964
3.5%
-0.32 1908
7.0%

RESULT_ZWheelDownAverageDiff__mm_
Real number (ℝ)

High correlation  Zeros 

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.05522232
Minimum-0.15
Maximum0.07
Zeros587
Zeros (%)2.1%
Negative26433
Negative (%)96.7%
Memory size427.3 KiB
2025-03-07T16:24:51.650305image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.15
5-th percentile-0.1
Q1-0.07
median-0.06
Q3-0.04
95-th percentile-0.01
Maximum0.07
Range0.22
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.026404697
Coefficient of variation (CV)-0.47815263
Kurtosis-0.084038953
Mean-0.05522232
Median Absolute Deviation (MAD)0.02
Skewness0.14956391
Sum-1510.22
Variance0.00069720804
MonotonicityNot monotonic
2025-03-07T16:24:51.839221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
-0.06 4026
14.7%
-0.05 3956
14.5%
-0.07 3805
13.9%
-0.04 3243
11.9%
-0.08 2784
10.2%
-0.03 2444
8.9%
-0.09 1922
7.0%
-0.02 1717
6.3%
-0.01 961
 
3.5%
-0.1 929
 
3.4%
Other values (13) 1561
 
5.7%
ValueCountFrequency (%)
-0.15 1
 
< 0.1%
-0.14 10
 
< 0.1%
-0.13 42
 
0.2%
-0.12 167
 
0.6%
-0.11 426
 
1.6%
-0.1 929
 
3.4%
-0.09 1922
7.0%
-0.08 2784
10.2%
-0.07 3805
13.9%
-0.06 4026
14.7%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.06 1
 
< 0.1%
0.05 2
 
< 0.1%
0.04 10
 
< 0.1%
0.03 20
 
0.1%
0.02 82
 
0.3%
0.01 212
 
0.8%
0 587
 
2.1%
-0.01 961
3.5%
-0.02 1717
6.3%

RESULT_ZWheelDownAngle__deg_
Real number (ℝ)

High correlation  Zeros 

Distinct58
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1570422
Minimum-0.41
Maximum0.19
Zeros297
Zeros (%)1.1%
Negative26610
Negative (%)97.3%
Memory size427.3 KiB
2025-03-07T16:24:51.979798image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.41
5-th percentile-0.27
Q1-0.21
median-0.16
Q3-0.11
95-th percentile-0.03
Maximum0.19
Range0.6
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.07463906
Coefficient of variation (CV)-0.47528028
Kurtosis-0.080103211
Mean-0.1570422
Median Absolute Deviation (MAD)0.05
Skewness0.15285391
Sum-4294.79
Variance0.0055709893
MonotonicityNot monotonic
2025-03-07T16:24:52.113896image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.16 1450
 
5.3%
-0.17 1442
 
5.3%
-0.19 1420
 
5.2%
-0.15 1412
 
5.2%
-0.14 1386
 
5.1%
-0.18 1378
 
5.0%
-0.2 1320
 
4.8%
-0.13 1312
 
4.8%
-0.21 1228
 
4.5%
-0.12 1218
 
4.5%
Other values (48) 13782
50.4%
ValueCountFrequency (%)
-0.41 3
 
< 0.1%
-0.4 2
 
< 0.1%
-0.39 5
 
< 0.1%
-0.38 7
 
< 0.1%
-0.37 16
 
0.1%
-0.36 22
 
0.1%
-0.35 47
0.2%
-0.34 59
0.2%
-0.33 76
0.3%
-0.32 115
0.4%
ValueCountFrequency (%)
0.19 1
 
< 0.1%
0.17 1
 
< 0.1%
0.14 1
 
< 0.1%
0.13 1
 
< 0.1%
0.12 3
 
< 0.1%
0.11 4
 
< 0.1%
0.1 4
 
< 0.1%
0.09 8
< 0.1%
0.08 6
 
< 0.1%
0.07 19
0.1%

RESULT_ZAngle__deg_
Real number (ℝ)

High correlation  Zeros 

Distinct53
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.099284774
Minimum-0.18
Maximum0.36
Zeros827
Zeros (%)3.0%
Negative2209
Negative (%)8.1%
Memory size427.3 KiB
2025-03-07T16:24:52.282062image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.18
5-th percentile-0.02
Q10.04
median0.1
Q30.15
95-th percentile0.23
Maximum0.36
Range0.54
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.077094885
Coefficient of variation (CV)0.7765026
Kurtosis-0.27482817
Mean0.099284774
Median Absolute Deviation (MAD)0.05
Skewness0.13086365
Sum2715.24
Variance0.0059436212
MonotonicityNot monotonic
2025-03-07T16:24:52.417452image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.09 1348
 
4.9%
0.1 1325
 
4.8%
0.12 1320
 
4.8%
0.08 1320
 
4.8%
0.11 1319
 
4.8%
0.07 1277
 
4.7%
0.05 1243
 
4.5%
0.06 1235
 
4.5%
0.14 1176
 
4.3%
0.13 1153
 
4.2%
Other values (43) 14632
53.5%
ValueCountFrequency (%)
-0.18 1
 
< 0.1%
-0.15 1
 
< 0.1%
-0.14 3
 
< 0.1%
-0.13 4
 
< 0.1%
-0.12 11
 
< 0.1%
-0.11 25
 
0.1%
-0.1 29
 
0.1%
-0.09 47
0.2%
-0.08 88
0.3%
-0.07 86
0.3%
ValueCountFrequency (%)
0.36 4
 
< 0.1%
0.35 7
 
< 0.1%
0.34 8
 
< 0.1%
0.33 22
 
0.1%
0.32 19
 
0.1%
0.31 40
 
0.1%
0.3 37
 
0.1%
0.29 76
0.3%
0.28 90
0.3%
0.27 138
0.5%

RESULT_ZAngleMax__deg_
Real number (ℝ)

High correlation 

Distinct77
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30976854
Minimum-0.41
Maximum0.65
Zeros130
Zeros (%)0.5%
Negative2518
Negative (%)9.2%
Memory size427.3 KiB
2025-03-07T16:24:52.565266image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.41
5-th percentile-0.27
Q10.29
median0.35
Q30.42
95-th percentile0.5
Maximum0.65
Range1.06
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.20310027
Coefficient of variation (CV)0.65565169
Kurtosis3.8527247
Mean0.30976854
Median Absolute Deviation (MAD)0.06
Skewness-2.1376331
Sum8471.55
Variance0.041249718
MonotonicityNot monotonic
2025-03-07T16:24:52.700199image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.35 1181
 
4.3%
0.34 1166
 
4.3%
0.32 1165
 
4.3%
0.37 1153
 
4.2%
0.33 1145
 
4.2%
0.36 1138
 
4.2%
0.38 1085
 
4.0%
0.31 1082
 
4.0%
0.3 1079
 
3.9%
0.39 1061
 
3.9%
Other values (67) 16093
58.8%
ValueCountFrequency (%)
-0.41 3
 
< 0.1%
-0.4 2
 
< 0.1%
-0.39 5
 
< 0.1%
-0.38 7
 
< 0.1%
-0.37 16
 
0.1%
-0.36 22
 
0.1%
-0.35 47
0.2%
-0.34 59
0.2%
-0.33 76
0.3%
-0.32 113
0.4%
ValueCountFrequency (%)
0.65 3
 
< 0.1%
0.64 1
 
< 0.1%
0.63 4
 
< 0.1%
0.62 11
 
< 0.1%
0.61 12
 
< 0.1%
0.6 16
 
0.1%
0.59 35
0.1%
0.58 31
 
0.1%
0.57 54
0.2%
0.56 78
0.3%

RESULT_InclinationBeltDirection__deg_
Real number (ℝ)

High correlation  Zeros 

Distinct44
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.075903174
Minimum-0.17
Maximum0.28
Zeros739
Zeros (%)2.7%
Negative1985
Negative (%)7.3%
Memory size427.3 KiB
2025-03-07T16:24:52.825672image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.17
5-th percentile-0.02
Q10.04
median0.08
Q30.11
95-th percentile0.17
Maximum0.28
Range0.45
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.056180846
Coefficient of variation (CV)0.74016464
Kurtosis0.11257403
Mean0.075903174
Median Absolute Deviation (MAD)0.04
Skewness0.018133825
Sum2075.8
Variance0.0031562874
MonotonicityNot monotonic
2025-03-07T16:24:52.948300image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.07 1993
 
7.3%
0.08 1957
 
7.2%
0.09 1945
 
7.1%
0.06 1909
 
7.0%
0.05 1765
 
6.5%
0.1 1761
 
6.4%
0.04 1617
 
5.9%
0.11 1497
 
5.5%
0.12 1484
 
5.4%
0.03 1416
 
5.2%
Other values (34) 10004
36.6%
ValueCountFrequency (%)
-0.17 1
 
< 0.1%
-0.14 1
 
< 0.1%
-0.13 1
 
< 0.1%
-0.12 6
 
< 0.1%
-0.11 8
 
< 0.1%
-0.1 23
 
0.1%
-0.09 27
 
0.1%
-0.08 44
0.2%
-0.07 76
0.3%
-0.06 106
0.4%
ValueCountFrequency (%)
0.28 8
 
< 0.1%
0.27 6
 
< 0.1%
0.26 13
 
< 0.1%
0.25 23
 
0.1%
0.24 36
 
0.1%
0.23 52
 
0.2%
0.22 80
 
0.3%
0.21 107
0.4%
0.2 162
0.6%
0.19 249
0.9%

RESULT_Inclination90ToBeltDirection__deg_
Real number (ℝ)

High correlation 

Distinct63
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.19524133
Minimum-0.55
Maximum0.09
Zeros75
Zeros (%)0.3%
Negative27147
Negative (%)99.3%
Memory size427.3 KiB
2025-03-07T16:24:53.085307image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.55
5-th percentile-0.35
Q1-0.25
median-0.19
Q3-0.13
95-th percentile-0.06
Maximum0.09
Range0.64
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.089452961
Coefficient of variation (CV)-0.4581661
Kurtosis-0.12411257
Mean-0.19524133
Median Absolute Deviation (MAD)0.06
Skewness-0.31142994
Sum-5339.46
Variance0.0080018323
MonotonicityNot monotonic
2025-03-07T16:24:53.252468image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.17 1221
 
4.5%
-0.15 1199
 
4.4%
-0.16 1196
 
4.4%
-0.14 1178
 
4.3%
-0.19 1175
 
4.3%
-0.18 1128
 
4.1%
-0.2 1126
 
4.1%
-0.21 1121
 
4.1%
-0.13 1042
 
3.8%
-0.22 1030
 
3.8%
Other values (53) 15932
58.3%
ValueCountFrequency (%)
-0.55 1
 
< 0.1%
-0.53 3
 
< 0.1%
-0.52 3
 
< 0.1%
-0.51 1
 
< 0.1%
-0.5 11
 
< 0.1%
-0.49 8
 
< 0.1%
-0.48 8
 
< 0.1%
-0.47 24
0.1%
-0.46 30
0.1%
-0.45 32
0.1%
ValueCountFrequency (%)
0.09 2
 
< 0.1%
0.07 2
 
< 0.1%
0.06 2
 
< 0.1%
0.05 4
 
< 0.1%
0.04 14
 
0.1%
0.03 25
 
0.1%
0.02 32
 
0.1%
0.01 45
 
0.2%
0 75
0.3%
-0.01 118
0.4%

RESULT_MaximalForceAngle__deg_
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21352457
Minimum0
Maximum0.57
Zeros134
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size427.3 KiB
2025-03-07T16:24:53.377268image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.09
Q10.15
median0.21
Q30.27
95-th percentile0.36
Maximum0.57
Range0.57
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.083838893
Coefficient of variation (CV)0.39264283
Kurtosis0.091989216
Mean0.21352457
Median Absolute Deviation (MAD)0.06
Skewness0.38754431
Sum5839.47
Variance0.0070289599
MonotonicityNot monotonic
2025-03-07T16:24:53.519182image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.19 1393
 
5.1%
0.2 1321
 
4.8%
0.17 1321
 
4.8%
0.16 1300
 
4.8%
0.18 1297
 
4.7%
0.15 1214
 
4.4%
0.21 1198
 
4.4%
0.22 1183
 
4.3%
0.24 1158
 
4.2%
0.23 1152
 
4.2%
Other values (47) 14811
54.2%
ValueCountFrequency (%)
0 134
 
0.5%
0.01 12
 
< 0.1%
0.02 20
 
0.1%
0.03 52
 
0.2%
0.04 92
 
0.3%
0.05 119
 
0.4%
0.06 175
 
0.6%
0.07 254
0.9%
0.08 328
1.2%
0.09 473
1.7%
ValueCountFrequency (%)
0.57 2
 
< 0.1%
0.55 1
 
< 0.1%
0.54 2
 
< 0.1%
0.53 5
 
< 0.1%
0.52 3
 
< 0.1%
0.51 9
 
< 0.1%
0.5 13
< 0.1%
0.49 13
< 0.1%
0.48 14
0.1%
0.47 32
0.1%

RESULT_InclinationInMaximalForceDirection__deg_
Real number (ℝ)

High correlation  Zeros 

Distinct43
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.070465848
Minimum-0.16
Maximum0.28
Zeros1664
Zeros (%)6.1%
Negative1710
Negative (%)6.3%
Memory size427.3 KiB
2025-03-07T16:24:53.644574image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum-0.16
5-th percentile-0.01
Q10.03
median0.07
Q30.11
95-th percentile0.16
Maximum0.28
Range0.44
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.054686555
Coefficient of variation (CV)0.77607177
Kurtosis0.01427141
Mean0.070465848
Median Absolute Deviation (MAD)0.04
Skewness0.13524578
Sum1927.1
Variance0.0029906193
MonotonicityNot monotonic
2025-03-07T16:24:53.765183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.06 1992
 
7.3%
0.07 1974
 
7.2%
0.08 1927
 
7.0%
0.09 1829
 
6.7%
0.05 1792
 
6.6%
0.04 1713
 
6.3%
0 1664
 
6.1%
0.1 1608
 
5.9%
0.03 1556
 
5.7%
0.11 1472
 
5.4%
Other values (33) 9821
35.9%
ValueCountFrequency (%)
-0.16 1
 
< 0.1%
-0.13 1
 
< 0.1%
-0.12 4
 
< 0.1%
-0.11 6
 
< 0.1%
-0.1 20
 
0.1%
-0.09 21
 
0.1%
-0.08 39
 
0.1%
-0.07 65
0.2%
-0.06 87
0.3%
-0.05 146
0.5%
ValueCountFrequency (%)
0.28 2
 
< 0.1%
0.27 7
 
< 0.1%
0.26 7
 
< 0.1%
0.25 15
 
0.1%
0.24 29
 
0.1%
0.23 42
 
0.2%
0.22 60
 
0.2%
0.21 98
0.4%
0.2 115
0.4%
0.19 207
0.8%

RESULT_Perpendicularity__mm_
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11323424
Minimum0
Maximum0.31
Zeros145
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size427.3 KiB
2025-03-07T16:24:53.876545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05
Q10.08
median0.11
Q30.14
95-th percentile0.19
Maximum0.31
Range0.31
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.04541115
Coefficient of variation (CV)0.40103726
Kurtosis0.084212354
Mean0.11323424
Median Absolute Deviation (MAD)0.03
Skewness0.39222743
Sum3096.73
Variance0.0020621725
MonotonicityNot monotonic
2025-03-07T16:24:53.990993image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.1 2528
 
9.2%
0.09 2378
 
8.7%
0.08 2312
 
8.5%
0.11 2286
 
8.4%
0.12 2152
 
7.9%
0.13 2026
 
7.4%
0.07 1878
 
6.9%
0.14 1745
 
6.4%
0.15 1508
 
5.5%
0.06 1363
 
5.0%
Other values (21) 7172
26.2%
ValueCountFrequency (%)
0 145
 
0.5%
0.01 54
 
0.2%
0.02 171
 
0.6%
0.03 320
 
1.2%
0.04 599
 
2.2%
0.05 987
3.6%
0.06 1363
5.0%
0.07 1878
6.9%
0.08 2312
8.5%
0.09 2378
8.7%
ValueCountFrequency (%)
0.31 2
 
< 0.1%
0.29 5
 
< 0.1%
0.28 8
 
< 0.1%
0.27 22
 
0.1%
0.26 24
 
0.1%
0.25 63
 
0.2%
0.24 104
 
0.4%
0.23 125
 
0.5%
0.22 213
0.8%
0.21 331
1.2%

Interactions

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2025-03-07T16:23:23.018607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:26.561957image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:29.664420image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:33.283314image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:36.308315image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:39.488220image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:43.187330image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:46.290708image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:49.580153image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:52.982249image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:56.185658image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:59.464594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:02.504301image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:05.589807image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:09.330329image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:12.459494image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:15.494224image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:18.432364image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:21.715561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:24.793417image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:28.889493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:32.251474image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:35.393929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:38.397395image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:41.568633image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:16.456949image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:19.788745image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:23.448029image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:26.698663image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:29.757526image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:33.374404image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:36.423282image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:39.582690image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:43.292498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:46.403271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:49.671463image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:53.080227image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:56.294504image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:59.560738image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:02.608419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:05.694245image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:09.468691image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:12.552404image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:15.584109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:18.543609image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:21.823053image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:24.922098image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:28.979631image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:32.370620image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:35.540113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:38.507938image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:41.667465image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:16.569491image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:19.888322image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:23.566956image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:26.798980image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:29.875948image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:33.489600image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:36.525060image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:39.683130image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:43.404887image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:46.524551image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:49.769837image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:53.239586image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:56.393840image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:59.679231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:02.721931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:05.798018image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:09.565904image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:12.688893image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:15.697853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:18.661741image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:21.917826image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:26.109732image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:29.113968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:32.529696image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:35.637689image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:38.659072image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:41.762243image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:16.658936image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:19.983093image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:23.682543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:26.893858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:29.990707image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:33.579564image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:36.620665image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:39.776288image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:43.529404image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:46.643682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:49.880862image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:53.362733image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:56.573203image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:59.792410image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:02.811721image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:05.889165image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:09.677372image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:12.783573image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:15.829137image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:18.753977image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:22.010741image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:26.199556image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:29.224085image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:32.630277image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:35.734876image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:38.750122image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:41.874107image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:16.748374image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:20.095843image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:23.797461image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:26.991561image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:30.103269image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:33.685494image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:36.753814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:39.893531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:43.616924image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:46.741762image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:49.990672image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:53.498164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:56.665888image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:23:59.927251image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:02.902158image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:05.979244image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:09.773192image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:12.874946image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:15.917355image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:18.863899image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:22.120836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:26.308504image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:29.332121image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:32.763891image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:35.892155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-03-07T16:24:38.857038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-03-07T16:24:54.112347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
AP3_2_Actual_Part_to_ServiceAP3_2_Settings_LeverArm_Machine_M353_Y_mmAP3_2_Settings_LeverArm_Machine_M354_X_mmPARAM_Inclination90ToBeltDirectionOffset__deg_PARAM_InclinationBeltDirectionOffset__deg_PARAM_M826FirstPosition__mm_PARAM_M826InsertLockPinPosition__mm_PARAM_M826SecondPosition__mm_PaletteRESULT_Inclination90ToBeltDirection__deg_RESULT_InclinationBeltDirection__deg_RESULT_InclinationInMaximalForceDirection__deg_RESULT_MaximalForceAngle__deg_RESULT_Perpendicularity__mm_RESULT_XAngleMax__deg_RESULT_XAngle__deg_RESULT_XWheelDownAngle__deg_RESULT_XWheelDownAverageDiff__mm_RESULT_XWheelDownAverageSensor1__mm_RESULT_XWheelDownAverageSensor2__mm_RESULT_XWheelUpAngle__deg_RESULT_XWheelUpAverageDiff__mm_RESULT_XWheelUpAverageSensor1__mm_RESULT_XWheelUpAverageSensor2__mm_RESULT_ZAngleMax__deg_RESULT_ZAngle__deg_RESULT_ZWheelDownAngle__deg_RESULT_ZWheelDownAverageDiff__mm_RESULT_ZWheelDownAverageSensor3__mm_RESULT_ZWheelDownAverageSensor4__mm_RESULT_ZWheelUpAngle__deg_RESULT_ZWheelUpAverageDiff__mm_RESULT_ZWheelUpAverageSensor3__mm_RESULT_ZWheelUpAverageSensor4__mm_
AP3_2_Actual_Part_to_Service1.0000.3510.2260.6770.6770.4360.4360.436-0.0010.0060.1880.1830.0360.036-0.235-0.225-0.235-0.235-0.214-0.102-0.215-0.214-0.203-0.1060.2600.2840.3000.299-0.221-0.2870.2580.257-0.218-0.252
AP3_2_Settings_LeverArm_Machine_M353_Y_mm0.3511.0001.0000.3170.3170.3910.3910.3910.0000.0690.0480.0500.0780.0770.1950.2010.1950.1950.3050.2890.2040.2040.3000.2830.1590.1530.1270.1240.2610.2620.1640.1650.2690.278
AP3_2_Settings_LeverArm_Machine_M354_X_mm0.2261.0001.0000.1670.1670.0960.0960.0960.0000.0180.0000.0000.0230.0230.0550.0590.0550.0510.3930.4780.0580.0590.3860.4650.0340.0390.0350.0340.1210.1460.0380.0360.1030.128
PARAM_Inclination90ToBeltDirectionOffset__deg_0.6770.3170.1671.0001.0001.0001.0001.0000.0000.1510.2240.2190.1750.1730.5100.5170.5100.5080.2190.2510.5160.5160.2080.2410.4800.4740.4040.3980.7790.7820.4980.4970.8700.795
PARAM_InclinationBeltDirectionOffset__deg_0.6770.3170.1671.0001.0001.0001.0001.0000.0000.1510.2240.2190.1750.1730.5100.5170.5100.5080.2190.2510.5160.5160.2080.2410.4800.4740.4040.3980.7790.7820.4980.4970.8700.795
PARAM_M826FirstPosition__mm_0.4360.3910.0961.0001.0001.0001.0001.0000.0000.2460.2820.2740.2930.2910.7200.7370.7200.7140.1700.3410.7470.7470.1770.3400.6590.6360.5300.5270.9830.9970.6860.6890.9960.992
PARAM_M826InsertLockPinPosition__mm_0.4360.3910.0961.0001.0001.0001.0001.0000.0000.2460.2820.2740.2930.2910.7200.7370.7200.7140.1700.3410.7470.7470.1770.3400.6590.6360.5300.5270.9830.9970.6860.6890.9960.992
PARAM_M826SecondPosition__mm_0.4360.3910.0961.0001.0001.0001.0001.0000.0000.2460.2820.2740.2930.2910.7200.7370.7200.7140.1700.3410.7470.7470.1770.3400.6590.6360.5300.5270.9830.9970.6860.6890.9960.992
Palette-0.0010.0000.0000.0000.0000.0000.0000.0001.000-0.0600.0770.0740.0800.081-0.035-0.036-0.035-0.035-0.024-0.004-0.037-0.037-0.025-0.0030.0470.0520.0580.0580.023-0.0100.0460.0460.011-0.019
RESULT_Inclination90ToBeltDirection__deg_0.0060.0690.0180.1510.1510.2460.2460.246-0.0601.0000.0090.020-0.939-0.9380.7130.7160.7130.7120.5850.2110.7160.7150.5920.222-0.099-0.088-0.069-0.0690.2370.202-0.099-0.1000.2060.169
RESULT_InclinationBeltDirection__deg_0.1880.0480.0000.2240.2240.2820.2820.2820.0770.0091.0000.9870.2380.238-0.234-0.233-0.234-0.234-0.0010.169-0.231-0.2300.0030.1740.8050.8690.8800.875-0.073-0.4800.8010.797-0.176-0.519
RESULT_InclinationInMaximalForceDirection__deg_0.1830.0500.0000.2190.2190.2740.2740.2740.0740.0200.9871.0000.2410.241-0.239-0.238-0.239-0.2390.0170.184-0.236-0.2350.0220.1890.8120.8770.8650.860-0.083-0.4900.8110.807-0.187-0.529
RESULT_MaximalForceAngle__deg_0.0360.0780.0230.1750.1750.2930.2930.2930.080-0.9390.2380.2411.0000.997-0.748-0.751-0.748-0.747-0.543-0.147-0.750-0.749-0.546-0.1550.3050.3120.2720.271-0.248-0.3170.3070.306-0.242-0.295
RESULT_Perpendicularity__mm_0.0360.0770.0230.1730.1730.2910.2910.2910.081-0.9380.2380.2410.9971.000-0.747-0.750-0.747-0.746-0.542-0.147-0.748-0.747-0.545-0.1550.3040.3120.2720.271-0.247-0.3170.3060.306-0.242-0.295
RESULT_XAngleMax__deg_-0.2350.1950.0550.5100.5100.7200.7200.720-0.0350.713-0.234-0.239-0.748-0.7471.0000.9971.0000.9980.512-0.0390.9900.9890.512-0.030-0.594-0.568-0.470-0.4680.7270.741-0.595-0.5940.6910.704
RESULT_XAngle__deg_-0.2250.2010.0590.5170.5170.7370.7370.737-0.0360.716-0.233-0.238-0.751-0.7500.9971.0000.9970.9960.517-0.0320.9970.9960.523-0.023-0.596-0.569-0.469-0.4670.7320.745-0.598-0.5960.6970.709
RESULT_XWheelDownAngle__deg_-0.2350.1950.0550.5100.5100.7200.7200.720-0.0350.713-0.234-0.239-0.748-0.7471.0000.9971.0000.9980.512-0.0390.9900.9890.512-0.030-0.594-0.568-0.470-0.4680.7270.741-0.595-0.5940.6910.704
RESULT_XWheelDownAverageDiff__mm_-0.2350.1950.0510.5080.5080.7140.7140.714-0.0350.712-0.234-0.239-0.747-0.7460.9980.9960.9981.0000.512-0.0380.9880.9870.512-0.030-0.593-0.568-0.470-0.4670.7260.740-0.595-0.5940.6900.703
RESULT_XWheelDownAverageSensor1__mm_-0.2140.3050.3930.2190.2190.1700.1700.170-0.0240.585-0.0010.017-0.543-0.5420.5120.5170.5120.5121.0000.8130.5190.5180.9880.812-0.116-0.106-0.106-0.1060.2480.218-0.114-0.1140.1940.168
RESULT_XWheelDownAverageSensor2__mm_-0.1020.2890.4780.2510.2510.3410.3410.341-0.0040.2110.1690.184-0.147-0.147-0.039-0.032-0.039-0.0380.8131.000-0.026-0.0250.7990.9900.2520.2480.2000.199-0.158-0.2150.2540.253-0.199-0.249
RESULT_XWheelUpAngle__deg_-0.2150.2040.0580.5160.5160.7470.7470.747-0.0370.716-0.231-0.236-0.750-0.7480.9900.9970.9900.9880.519-0.0261.0000.9980.531-0.015-0.596-0.567-0.465-0.4630.7340.744-0.597-0.5960.7000.710
RESULT_XWheelUpAverageDiff__mm_-0.2140.2040.0590.5160.5160.7470.7470.747-0.0370.715-0.230-0.235-0.749-0.7470.9890.9960.9890.9870.518-0.0250.9981.0000.530-0.015-0.595-0.566-0.464-0.4620.7330.743-0.596-0.5950.6990.709
RESULT_XWheelUpAverageSensor1__mm_-0.2030.3000.3860.2080.2080.1770.1770.177-0.0250.5920.0030.022-0.546-0.5450.5120.5230.5120.5120.9880.7990.5310.5301.0000.813-0.110-0.105-0.115-0.1150.2530.226-0.107-0.1070.1930.162
RESULT_XWheelUpAverageSensor2__mm_-0.1060.2830.4650.2410.2410.3400.3400.340-0.0030.2220.1740.189-0.155-0.155-0.030-0.023-0.030-0.0300.8120.990-0.015-0.0150.8131.0000.2620.2500.1930.191-0.158-0.2080.2640.263-0.208-0.261
RESULT_ZAngleMax__deg_0.2600.1590.0340.4800.4800.6590.6590.6590.047-0.0990.8050.8120.3050.304-0.594-0.596-0.594-0.593-0.1160.252-0.596-0.595-0.1100.2621.0000.9690.8380.834-0.566-0.8030.9980.993-0.611-0.861
RESULT_ZAngle__deg_0.2840.1530.0390.4740.4740.6360.6360.6360.052-0.0880.8690.8770.3120.312-0.568-0.569-0.568-0.568-0.1060.248-0.567-0.566-0.1050.2500.9691.0000.9330.928-0.501-0.8000.9680.964-0.565-0.819
RESULT_ZWheelDownAngle__deg_0.3000.1270.0350.4040.4040.5300.5300.5300.058-0.0690.8800.8650.2720.272-0.470-0.469-0.470-0.470-0.1060.200-0.465-0.464-0.1150.1930.8380.9331.0000.993-0.357-0.7140.8300.827-0.441-0.681
RESULT_ZWheelDownAverageDiff__mm_0.2990.1240.0340.3980.3980.5270.5270.5270.058-0.0690.8750.8600.2710.271-0.468-0.467-0.468-0.467-0.1060.199-0.463-0.462-0.1150.1910.8340.9280.9931.000-0.354-0.7100.8260.822-0.438-0.677
RESULT_ZWheelDownAverageSensor3__mm_-0.2210.2610.1210.7790.7790.9830.9830.9830.0230.237-0.073-0.083-0.248-0.2470.7270.7320.7270.7260.248-0.1580.7340.7330.253-0.158-0.566-0.501-0.357-0.3541.0000.873-0.569-0.5670.9440.841
RESULT_ZWheelDownAverageSensor4__mm_-0.2870.2620.1460.7820.7820.9970.9970.997-0.0100.202-0.480-0.490-0.317-0.3170.7410.7450.7410.7400.218-0.2150.7440.7430.226-0.208-0.803-0.800-0.714-0.7100.8731.000-0.802-0.7990.8790.937
RESULT_ZWheelUpAngle__deg_0.2580.1640.0380.4980.4980.6860.6860.6860.046-0.0990.8010.8110.3070.306-0.595-0.598-0.595-0.595-0.1140.254-0.597-0.596-0.1070.2640.9980.9680.8300.826-0.569-0.8021.0000.995-0.613-0.863
RESULT_ZWheelUpAverageDiff__mm_0.2570.1650.0360.4970.4970.6890.6890.6890.046-0.1000.7970.8070.3060.306-0.594-0.596-0.594-0.594-0.1140.253-0.596-0.595-0.1070.2630.9930.9640.8270.822-0.567-0.7990.9951.000-0.611-0.859
RESULT_ZWheelUpAverageSensor3__mm_-0.2180.2690.1030.8700.8700.9960.9960.9960.0110.206-0.176-0.187-0.242-0.2420.6910.6970.6910.6900.194-0.1990.7000.6990.193-0.208-0.611-0.565-0.441-0.4380.9440.879-0.613-0.6111.0000.903
RESULT_ZWheelUpAverageSensor4__mm_-0.2520.2780.1280.7950.7950.9920.9920.992-0.0190.169-0.519-0.529-0.295-0.2950.7040.7090.7040.7030.168-0.2490.7100.7090.162-0.261-0.861-0.819-0.681-0.6770.8410.937-0.863-0.8590.9031.000

Missing values

2025-03-07T16:24:42.049884image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-07T16:24:42.631846image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PaletteAP3_2_Settings_LeverArm_Machine_M353_Y_mmAP3_2_Settings_LeverArm_Machine_M354_X_mmAP3_2_Settings_LeverArm_Machine_M355_Z_mmAP3_2_Actual_Part_to_ServiceAP3_2_Settings_M358_Montage_Position_mmAP3_2_Settings_M359_Montage_Position_mmPARAM_InclinationBeltDirectionOffset__deg_PARAM_Inclination90ToBeltDirectionOffset__deg_PARAM_M826FirstPosition__mm_PARAM_M826SecondPosition__mm_PARAM_M826InsertLockPinPosition__mm_RESULT_XWheelUpAverageSensor1__mm_RESULT_XWheelUpAverageSensor2__mm_RESULT_XWheelUpAverageDiff__mm_RESULT_XWheelUpAngle__deg_RESULT_XWheelDownAverageSensor1__mm_RESULT_XWheelDownAverageSensor2__mm_RESULT_XWheelDownAverageDiff__mm_RESULT_XWheelDownAngle__deg_RESULT_XAngle__deg_RESULT_XAngleMax__deg_RESULT_ZWheelUpAverageSensor3__mm_RESULT_ZWheelUpAverageSensor4__mm_RESULT_ZWheelUpAverageDiff__mm_RESULT_ZWheelUpAngle__deg_RESULT_ZWheelDownAverageSensor3__mm_RESULT_ZWheelDownAverageSensor4__mm_RESULT_ZWheelDownAverageDiff__mm_RESULT_ZWheelDownAngle__deg_RESULT_ZAngle__deg_RESULT_ZAngleMax__deg_RESULT_InclinationBeltDirection__deg_RESULT_Inclination90ToBeltDirection__deg_RESULT_MaximalForceAngle__deg_RESULT_InclinationInMaximalForceDirection__deg_RESULT_Perpendicularity__mm_
02.087.3159.8141.5513370.01.9-17.05-0.050.52.0-8.5-8.50.210.40-0.19-0.540.160.40-0.24-0.68-0.61-0.68-0.52-0.670.160.45-0.56-0.52-0.04-0.120.160.450.11-0.110.150.110.08
13.087.3159.8141.5513371.01.9-17.05-0.050.52.0-8.5-8.50.390.57-0.18-0.520.330.57-0.23-0.66-0.59-0.66-0.53-0.660.130.37-0.56-0.49-0.07-0.210.080.370.03-0.090.090.030.05
24.087.3159.8141.5513372.01.9-17.05-0.050.52.0-8.5-8.50.230.40-0.18-0.500.180.41-0.23-0.65-0.58-0.65-0.55-0.700.150.43-0.60-0.54-0.06-0.160.130.430.08-0.070.110.080.06
35.087.3159.8141.5513373.01.9-17.05-0.050.52.0-8.5-8.50.190.41-0.22-0.630.130.41-0.28-0.80-0.71-0.80-0.53-0.670.130.37-0.58-0.51-0.07-0.200.090.370.04-0.210.210.030.11
46.087.3159.8141.5513374.01.9-17.05-0.050.52.0-8.5-8.50.190.39-0.20-0.570.140.39-0.25-0.72-0.64-0.72-0.55-0.670.120.33-0.59-0.52-0.07-0.210.060.330.01-0.140.140.010.07
63.087.3159.8141.5513376.01.9-17.05-0.050.52.0-8.5-8.50.050.33-0.28-0.79-0.010.33-0.33-0.94-0.86-0.94-0.53-0.640.110.31-0.58-0.51-0.07-0.200.060.310.01-0.360.360.000.19
74.087.3159.8141.5513377.01.9-17.05-0.050.52.0-8.5-8.50.200.39-0.18-0.520.150.38-0.23-0.67-0.59-0.67-0.55-0.650.110.30-0.57-0.50-0.07-0.200.050.300.00-0.090.090.000.05
85.087.3159.8141.5513378.01.9-17.05-0.050.52.0-8.5-8.50.280.49-0.21-0.600.230.48-0.25-0.72-0.66-0.72-0.53-0.660.140.39-0.57-0.51-0.06-0.180.100.390.06-0.160.170.050.09
96.087.3159.8141.5513379.01.9-17.05-0.050.52.0-8.5-8.50.290.49-0.20-0.560.240.49-0.25-0.71-0.63-0.71-0.56-0.670.120.34-0.59-0.51-0.08-0.230.050.340.01-0.130.130.000.07
107.087.3159.8141.5513380.01.9-17.05-0.050.52.0-8.5-8.50.160.41-0.25-0.700.110.41-0.29-0.84-0.77-0.84-0.53-0.670.140.40-0.57-0.52-0.06-0.160.120.400.07-0.270.270.060.15
PaletteAP3_2_Settings_LeverArm_Machine_M353_Y_mmAP3_2_Settings_LeverArm_Machine_M354_X_mmAP3_2_Settings_LeverArm_Machine_M355_Z_mmAP3_2_Actual_Part_to_ServiceAP3_2_Settings_M358_Montage_Position_mmAP3_2_Settings_M359_Montage_Position_mmPARAM_InclinationBeltDirectionOffset__deg_PARAM_Inclination90ToBeltDirectionOffset__deg_PARAM_M826FirstPosition__mm_PARAM_M826SecondPosition__mm_PARAM_M826InsertLockPinPosition__mm_RESULT_XWheelUpAverageSensor1__mm_RESULT_XWheelUpAverageSensor2__mm_RESULT_XWheelUpAverageDiff__mm_RESULT_XWheelUpAngle__deg_RESULT_XWheelDownAverageSensor1__mm_RESULT_XWheelDownAverageSensor2__mm_RESULT_XWheelDownAverageDiff__mm_RESULT_XWheelDownAngle__deg_RESULT_XAngle__deg_RESULT_XAngleMax__deg_RESULT_ZWheelUpAverageSensor3__mm_RESULT_ZWheelUpAverageSensor4__mm_RESULT_ZWheelUpAverageDiff__mm_RESULT_ZWheelUpAngle__deg_RESULT_ZWheelDownAverageSensor3__mm_RESULT_ZWheelDownAverageSensor4__mm_RESULT_ZWheelDownAverageDiff__mm_RESULT_ZWheelDownAngle__deg_RESULT_ZAngle__deg_RESULT_ZAngleMax__deg_RESULT_InclinationBeltDirection__deg_RESULT_Inclination90ToBeltDirection__deg_RESULT_MaximalForceAngle__deg_RESULT_InclinationInMaximalForceDirection__deg_RESULT_Perpendicularity__mm_
276894.087.0159.8141.556540.01.9-17.050.010.37-3.0-13.5-13.50.330.48-0.15-0.420.270.48-0.21-0.60-0.51-0.60-0.38-0.470.090.25-0.42-0.35-0.07-0.190.030.250.04-0.140.140.040.07
276905.087.0159.8141.556541.01.9-17.050.010.37-3.0-13.5-13.50.340.50-0.16-0.460.280.50-0.21-0.60-0.53-0.60-0.36-0.490.130.37-0.38-0.32-0.06-0.170.100.370.11-0.160.190.110.10
276916.087.0159.8141.556542.01.9-17.050.010.37-3.0-13.5-13.50.420.59-0.17-0.480.370.58-0.22-0.62-0.55-0.62-0.38-0.500.120.35-0.41-0.33-0.09-0.240.050.350.06-0.180.180.060.10
276927.087.0159.8141.556543.01.9-17.050.010.37-3.0-13.5-13.50.310.47-0.16-0.450.270.48-0.21-0.61-0.53-0.61-0.36-0.500.130.38-0.40-0.36-0.04-0.120.130.380.14-0.160.210.140.11
276938.087.0159.8141.556544.01.9-17.050.010.37-3.0-13.5-13.50.430.56-0.13-0.380.360.55-0.19-0.55-0.47-0.55-0.37-0.500.130.37-0.39-0.33-0.06-0.180.090.370.11-0.090.140.100.07
276941.087.0159.8141.556545.01.9-17.050.010.37-3.0-13.5-13.50.360.51-0.15-0.420.300.51-0.21-0.60-0.51-0.60-0.36-0.500.130.38-0.40-0.36-0.05-0.130.120.380.13-0.140.190.130.10
276952.087.0159.8141.556546.01.9-17.050.010.37-3.0-13.5-13.50.300.47-0.18-0.500.240.47-0.23-0.66-0.58-0.66-0.36-0.470.110.30-0.41-0.32-0.09-0.260.020.300.03-0.210.200.030.11
276963.087.0159.8141.556547.01.9-17.050.010.37-3.0-13.5-13.50.260.44-0.17-0.490.230.45-0.22-0.63-0.56-0.63-0.39-0.480.100.28-0.42-0.36-0.06-0.180.050.280.06-0.190.190.050.10
276974.087.0159.8141.556548.01.9-17.050.010.37-3.0-13.5-13.50.270.44-0.16-0.470.220.43-0.22-0.62-0.54-0.62-0.35-0.490.140.40-0.38-0.34-0.05-0.130.140.400.15-0.170.220.140.12
276985.087.0159.8141.556549.01.9-17.050.010.37-3.0-13.5-13.50.300.48-0.18-0.510.240.48-0.23-0.66-0.59-0.66-0.36-0.490.130.38-0.39-0.35-0.04-0.120.130.380.14-0.210.250.130.13